Friedman test

Friedman test

The Friedman test tests the Nullhypothesis of identical populations for dependent data. It is an equivalent to the one factorial variance analysis with repeated measurement without making any assumptions on the distributions of the populations. It uses only the rank information of the data.

Requirements:

Data must be ordinal (rank-order) scaled. Distribution is free.

 

Hypothesis:

H0:      The treatments have identical effects

H1:      At least one treatment is different from at least one other treatment

Idea:

The data of k subjects and p treatments are first displayed in a two dimensional table. Then the data of each person are brought into one rank order (treatment ranks). This procedure ensures that the dependency of the data is taken into account. Next for each treatment the sum of ranks Ti is computed. Whereas the total sum of ranks is:

 

Total sum of ranks

 

with

k = number of subjects

p = number of treatments

 

For sufficiently large sample sizes (k>10 and p>4) the following value is approximately Chi-Square distributed with p-1 degrees of freedom:

 

formula Friedman test

 

if there are tied ranks Chi-Square is computed the following way:

 

formula Friedman test with tied ranks

 

wheras

 

A

 

C

 

Conover recommends the following F statistic, which has a more accurate approximation:

 

formula Conover F statistic for Friedman test

 

with

 

degrees of freedom Conover F statistic for Friedman test degrees of freedom

 


Post-hoc analysis

If the H0 is rejected it is often desirable to know which treatments are different from each other. Treatments i and j are different if the difference between the two mean ranks meets the following requirment:

 

formula post-hoc analysis for Friedman test

 

whereas:

 

A

 

critcal t-value = critical t-value with (k-1)(p-1) degrees of freedom

 


Example of a Friedman test

In a poll 10 subjects rated 4 different paintings on a scale from 0 (don’t like it at all) to 5 (like it very much). The following table shows the data and ranks for all tubjects and paintings:

 

  Paintings

1
Rank
2
Rank
3
Rank
4
Rank
1
0
1
5
4
1
2
4
3
2
3
2
4
3
2
1
5
4
3
1
1
4
3.5
3
2
4
3.5
4
4
4
2
1.5
2
1.5
3
3
5
2
1.5
2
1.5
4
4
3
3
6
0
1
3
2
5
3.5
5
3.5
7
3
2.5
1
1
3
2.5
4
4
8
5
3.5
3
2
1
1
5
3.5
9
1
1
5
4
2
2
4
3
10
2
2
4
4
0
1
3
3
Ti

19.5

26.5

20.5

33.5
Mean rank

1.95

2.65

2.05

3.35

We then get for the total sum of ranks:

 

Total sum of ranks

 

We have tied ranks so the Chi-square value is:

 

formula Friedman test with tied ranks

 

whereas

 

A

 

C

 

so the Chi-square value is:

 

formula Friedman test with tied ranks

 

The critical 5% Chi-square for 3 degrees of freedom is 7.81. The Chi-square test does reject H0. There must be at least one painting which is differentially rated compared to at least one other painting.

 

For Conovers’ F value we get:

 

Conover F statistic for Friedman test

 

and the 5% critical F value with 3 and 27 degrees of freedom is 2.96. The F Test does reject H0. There must be at least one painting which is differentially rated compared to at least one other painting.

The critical Difference of the Mean Ranks Mean rank is:

 

formula post-hoc analysis for Friedman test

 

formula post-hoc analysis for Friedman test

 

The following table shows the differences of the mean ranks:

 


Painting 1
Painting 2
Painting 3
Painting 2
0.7
-
-
Painting 3
0.1
0.6
-
Painting 4
1.4 *
0.7
1.3 *

 

Post hoc paired comparisons reveal that painting four is rated differentially compared to paintings 1 and 3.

 

BrightStat output of Friedman test example

 

This is a fictitious example.


Wiki link Friedman test


References

Bortz, J. (2005). Statistik für Human- und Sozialwissenschaftler (6th Edition). Heidelberg: Springer Medizin Verlag.

Conover, W.J. (1999). Practical nonparametric Statistics.(3rd edition). Wiley.

Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association (American Statistical Association), 32 (200), 675 – 701. doi:10.2307/2279372

Friedman, M. (1939). A correction: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association (American Statistical Association), 34 (205), 109. doi:10.2307/2279169

Schaich, H.E. & Hamerle, A. (1984). Verteilungsfreie statistische Prüfverfahren, Berlin.




 

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